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                Field
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                under multiple environmental and socio-economic scenarios. You’ll develop sought-after skills in geospatial analysis, hydrodynamics, sediment transport, machine learning-assisted detection, and hydro 
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                for complex data accessible to the scientific community and to produce innovative methodology related to trial designs, longitudinal and event history data, precision medicine, causal inference, AI/machine 
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                develop AI- and deep learning–based computer vision tools to automatically identify and quantify intertidal organisms. Beyond computer vision, it will leverage machine learning for large-scale, data-driven 
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                This PhD project focuses on advancing computer vision and edge-AI technology for real-time marine monitoring. In collaboration with CEFAS (the Centre for Environment, Fisheries, and Aquaculture 
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                * Theoretical foundations of machine learning The group has strong ties with the Centre for Discrete Mathematics and its Applications (DIMAP), established in 2007 jointly with Warwick Mathematics Institute and 
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                intelligence, NLP, machine learning, or a related field Experience with Python and Generative AI libraries (e.g., Huggingface Transformers) Knowledge of Multimodal Generative AI models and their corresponding 
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                built to identify and correct errors, apply bias adjustments, and assess data quality. State-of-the-art multisource blending methods will then be applied (e.g. kriging, probabilistic merging, machine 
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                and kinematic models with machine-learning-based channel state information (CSI) prediction to enable robust, low-latency connectivity across multi-layer NTN systems. This PhD project sits 
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                the investigation and realization of improved microwave probe design, data processing, and visualization techniques to provide a robust method of data analysis, flaw characterization and sizing. AI/machine learning 
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                including machine learning. This research will support the path to net zero flights and there will be opportunities to become involved in practical aspects of fuel system design and testing during their PhD